Spying on small wildlife sounds using affordable collar-mounted miniature microphones: an innovative method to record individual daylong vocalisations in chipmunks
Bibliographic record
Abstract
Technological advances can greatly benefit the scientific community by making new areas of research accessible. The study of animal vocal communication, in particular, can gain new insights and knowledge from technological improvements in recording equipment. Our comprehension of the acoustic signals emitted by animals would be greatly improved if we could continuously track the daily natural emissions of individuals in the wild, especially in the context of integrating individual variation into evolutionary ecology research questions. We show here how this can be accomplished using an operational tiny audio recorder that can easily be fitted as an on-board acoustic data-logger on small free-ranging animals. The high-quality 24 h acoustic recording logged on the spy microphone device allowed us to very efficiently collect daylong chipmunk vocalisations, giving us much more detailed data than the classical use of a directional microphone over an entire field season. The recordings also allowed us to monitor individual activity patterns and record incredibly long resting heart rates, and to identify self-scratching events and even whining from pre-emerging pups in their maternal burrow.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".